The ToM QA Dataset is designed to evaluate question-answering models' ability to reason about beliefs. It includes 3 task types and 4 question types, creating 12 total scenarios. The dataset is inspired by theory-of-mind experiments in developmental psychology and is used to test models' understanding of beliefs and inconsistent states of the world.
The ToM QA Dataset, introduced in the EMNLP 2018 paper 'Evaluating Theory of Mind in Question Answering', provides a comprehensive set of scenarios to test question-answering models. The dataset includes first-order and second-order belief questions, as well as memory and reality questions, to ensure models have a correct understanding of the state of the world and others' beliefs. It is available in four versions: easy with noise, easy without noise, hard with noise, and hard without noise.
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) uses epidemiological, behavioral, and neuroimaging data to understand how individuals can best retain cognitive abilities into old age. The Cam-CAN Data Access Portal provides access to datasets from the Cambridge Centre for Ageing and Neuroscience, including neuroimaging and cognitive data from participants aged 18-90.
Psychology LLM、LLM、The Big Model of Mental Health、Finetune、InternLM2、InternLM2.5、Qwen、ChatGLM、Baichuan、DeepSeek、Mixtral、LLama3、GLM4、Qwen2 - SmartFlowAI/EmoLLM
The Emotional First Aid Raw Dataset is a collection of raw, unannotated psychological counseling Q&A data, designed to support research in AI applications for mental health. It contains over 172,000 topics with 2,381,273 messages, totaling 44,514,786 characters, providing a rich source of data for natural language processing and AI development.